Energy-Based Generative Planning for Autonomous Driving

The central goal of autonomous driving is to combine human-level driving performance with reliable decision-making under multiple safety and behavioral constraints. In this paper, we introduce **EnergyDrive**, an energy-based generative planning framework for end-to-end autonomous driving that supports interpretable planning, flexible sampling, and compositional control. EnergyDrive learns an explicit, scene-conditioned trajectory energy landscape from expert demonstrations, rather than time-dependent transport fields typically learned by diffusion- and flow-based generative planners. This provides a shared representation for trajectory generation and objective composition, with repulsive sampling to explore diverse driving trajectories. At inference time, the learned driving prior and modular energies for target speed, drivable-area compliance, and collision avoidance jointly define the planning objective. EnergyDrive outperforms the compared imitation-learning baselines on the NAVSIM v2 *navhard* and HUGSIM. Qualitative results demonstrate flexible sampling over driving behavior through energy composition. Energy landscape visualizations show the interpretability and reveal how learned preferences and individual constraints shape the resulting plans. Our website is available at https://iclr2027-16699-author.github.io/